Titlebook: Database Systems for Advanced Applications; 25th International C Yunmook Nah,Bin Cui,Steven Euijong Whang Conference proceedings 2020 Sprin

foreign

EPARS: Early Prediction of At-Risk Students with Online and Offline Learning Behaviorsks mostly rely on either online or offline learning behaviors which are not comprehensive enough to capture the whole learning processes and lead to unsatisfying prediction performance. We propose a novel algorithm (EPARS) that could early predict STAR in a semester by modeling online and offline le

传授知识

MRMRP: Multi-source Review-Based Model for Rating Predictionfew years, many studies in recommender systems take user reviews into consideration and achieve promising performance. However, in daily life, most consumers are used to leaving no comments for products purchased and most reviews written by consumers are short, which leads to the performance degrada

CORD

Debark

Few-Shot Human Activity Recognition on Noisy Wearable Sensor Datanew-class activities unseen during training from a few samples. Very few researches of few-shot learning (FSL) have been done in HAR to address the above problem, though FSL has been widely used in computer vision tasks. Besides, it is impractical to annotate sensor data with accurate activity label

绿州

Adversarial Generation of Target Review for Rating Predictionisting methods learn the latent representations from the user’s and the item’s historical reviews, and then combine these two representations for rating prediction. The fatal limitation in these methods is that they are unable to utilize the most predictive review of the target user for the target i

微粒

Hybrid Attention Based Neural Architecture for Text Semantics Similarity Measurementguage processing. It is a complicated task due to the ambiguity and variability of linguistic expression. Previous studies focus on modeling the representation of a sentence in multiple granularities and then measure the similarity based on the representations. However, above methods cannot make ful

卡死偷电

Instance Explainable Multi-instance Learning for ROI of Various Dataion can be generalized to the bag containing multiple data instances, i.e., identify the instances that probably arouse our interest. Under the circumstance without instance labels, generalized ROI estimation problem can be addressed in the framework of Multi-Instance Learning (MIL). MIL is a variat

BRAWL

无王时期,

pulmonary-edema

Progressive Term Frequency Analysis on Large Text Collectionsep up with the growth of data. The delays when processing huge textual data can negatively impact user activity and insight. This calls for a paradigm shift from blocking fashion to progressive processing. In this paper, we propose a sample-based progressive processing model that focuses on term fre